Method and system for robotic assembly parameter optimization

ABSTRACT

A method and system to optimize the parameters of a robot used in an assembly process. The assembly process is categorized based on its nature which may be cylindrical, radial and multi-stage insertion. The search pattern and search parameters are specified. The parameters are optimized and the optimized parameter set are verified and when a predetermined criteria such as assembly cycle time set and/or success rate is met the optimization process stops. When the optimization stops the verified parameters are used to cause the robot to perform the categorized assembly process. If the parameters do not meet the predetermined criteria, another round of optimization using the same or other parameters can be performed.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the priority of U.S. provisional patentapplication Ser. No. 60/879,483 filed on Jan. 9, 2007, entitled “MethodAnd System For Robotic Assembly Parameter Optimization” the contents ofwhich are relied upon and incorporated herein by reference in theirentirety, and the benefit of priority under 35 U.S.C. 119(e) is herebyclaimed.

FIELD OF THE INVENTION

This invention relates to a robotic assembly process and optimization ofthe parameters of the robot.

DESCRIPTION OF THE PRIOR ART

Industrial robots with force control are used more and more in assemblyapplications in both the automotive and general industries. U.S. PatentApplication Publication No. 2002056181 (“the '181 PublishedApplication”) and U.S. Patent Application Publication No. 2005113971(“the '971 Published Application”) both describe the use of anindustrial robot with force control to perform tight-tolerance assemblytasks that cannot be performed by a conventional industrial robot withposition control.

As is described in the '181 Published Application and with reference toFIGS. 3a to 3d therein, a robot after being positioned at an approachstart position for fitting a fitting part into a receiving part startsan approach operation to the received part under speed and forcecontrol. When the fitting part comes in contact with the receiving partand a reaction force thereof exceeds a threshold value set by parameter,it is determined that the fitting part comes in contact with thereceiving part, and the procedure proceeds to a fitting operation. Inthe fitting operation, the fitting part is moved under speed and forcecontrol in accordance with the inserting/fitting direction. If thefitting operation does not progress for some reason and the movementstops in the middle of the fitting the fitting part is moved in adirection opposite to the inserting direction up to the approach startposition under the speed and force control in accordance with the speedand the target force for the fitting operation to thereby draw thefitting part from the receiving part. FIGS. 4a to 4c show the drawing ofa fitted part from a receiving part. The set speed for the removal ofthe fitted part from the receiving part is under force control.

As is described in the '971 Published Application, an attraction forcevector is superimposed on the force measured by a force sensor on therobot as a fitting part is moved toward a receiving part. The forcevector can also be a repulsive force vector. After the robot endeffector attracts contact with the surface on which the receiving partis located the force/torque values are converted into a velocity commandvalue and parameters are designed for stable and gentle contact of theend effector with the surface to thereby minimize the contact force ofthe fitting part with the surface. A search velocity pattern in a planeparallel to the surface by the controller on the velocity command to therobot when the fitting part is in contact with the surface and thelocation of the receiving part is not known to the robot controller.

However, since the introduction of force control, in which the actualrobot path depends not only on the programmed position but also on theinteraction force between the assembled parts/components, the process ofprogramming the robot has become more difficult. The optimal set ofrobotic (force control) parameters is often obtained either by trial anderror or offline analysis tools. This parameter selection process istedious and time consuming.

SUMMARY OF THE INVENTION

A method of optimizing the parameters of controlling a robot to fit afitting part into a receiving part that has the steps of:

categorizing independent of a size of the fitting part and a size of thereceiving part an assembly process for the robot to fit the fitting partinto the receiving part as one of a plurality of predetermined types ofthe assembly processes;

specifying from the categorized assembly process a search pattern forfitting the fitting part into the receiving part and parameters for thecategorized assembly process;

obtaining by using a predetermined technique a set of optimal parametersfor the categorized assembly process;

verifying that the set of optimal parameters meets a predeterminedcriteria to become the optimal parameters for controlling the robot toperform the search pattern specified from the categorized assemblyprocess; and

controlling the robot to perform the specified search pattern using theverified optimal parameters for the categorized assembly process tocreate an assembly that is a combination of the fitting and receivingparts.

A method of optimizing the parameters of controlling a robot to fit afitting part into a receiving part has the steps of:

categorizing from a plurality of predetermined types of assemblyprocesses independent of a size of the fitting part and a size of areceiving part an assembly process for the robot to fit the fitting partinto the receiving part to create an assembly that is a combination ofthe fitting and receiving parts;

specifying from the categorized assembly process a search pattern forthe categorized assembly process and parameters for the categorizedassembly process for fitting the fitting part into the receiving part tocreate the assembly that is the combination of the fitting and receivingparts;

obtaining by using a predetermined technique a set of optimal parametersfor the categorized assembly process;

verifying that the set of optimal parameters meets a predeterminedcriteria to become the optimal parameters for controlling the robot toperform the search pattern specified from the categorized assemblyprocess; and

controlling the robot to perform the specified search pattern forfitting the fitting part into the receiving part to create the assemblythat is the combination of the fitting and receiving parts using theverified optimal parameters for the categorized assembly process.

A method of optimizing the parameters of controlling a robot to fit afitting part into a receiving part has the steps of:

categorizing based on a characteristic of the fitting part and thereceiving part other than a size of the fitting part and a size of thereceiving part an assembly process for the robot to fit the fitting partinto the receiving part as one of a plurality of predetermined types ofthe assembly processes;

specifying from the categorized assembly process a search pattern forfitting the fitting part into the receiving part and parameters for thecategorized assembly process;

obtaining by using a predetermined technique a set of optimal parametersfor the categorized assembly process;

verifying that the set of optimal parameters meets a predeterminedcriteria to become the optimal parameters for controlling the robot toperform the search pattern specified from the categorized assemblyprocess; and

controlling the robot to perform the specified search pattern using theverified optimal parameters for the categorized assembly process tocreate an assembly that is a combination of the fitting and receivingparts.

DESCRIPTION OF THE DRAWING

FIG. 1 shows a flowchart for the robot assembly parameter optimizationmethod of the present invention.

FIG. 2 illustrates an insertion process.

FIG. 3 illustrates the assembly of a torque converter as an example of aradial assembly process.

FIG. 4 shows the spiral searching pattern for cylindrical insertion.

FIG. 5 shows the combined search path for a radial type insertion.

FIG. 6 shows an example of a DOE process analysis diagram.

FIG. 7 shows an example of a DOE Analysis of variance diagram.

FIG. 8 shows the graphical user interface (GUI) for the main page of acomputer program is one embodiment of the present invention.

FIG. 9 shows the GUI for the position setup page.

FIG. 10 shows the GUI for the set parameter page.

FIG. 11 shows the GUI for the tune parameter page.

FIG. 12 shows the GUI for the screening result page.

FIG. 13 shows the GUI for the Pareto plot page.

FIG. 14 shows the GUI for the optimization result page.

FIG. 15 shows the GUI for the main effect plot page.

FIG. 16 shows the GUI for the verification result page.

FIG. 17 shows the GUI for the assembly time distribution plot page.

FIG. 18 shows a system which may be used to implement the presentinvention.

DETAILED DESCRIPTION

Referring now to FIG. 1, there is shown a flowchart 100 for the methodof the present invention for robotic assembly parameter optimization.The flowchart 100 has four action steps 101-104, each of which aredescribed in more detail below, and a decision step 105. The first step101 in the method is the assembly process categorization. The secondstep 102 is the search pattern and search parameter specification. Thethird step 103 is the Design Of Experiment (DOE) based optimization andthe fourth step 104 is that of parameter set verification. If in step105 the result is satisfied, such as the assembly cycle time and/orsuccess rate meet the required values, the process stops at 106otherwise another round of optimization may be performed. While FIG. 1shows in step 103 a DOE based optimization and DOE based optimization isdescribed hereinafter, it should be appreciated that DOE is only oneexample of an optimization type that can be used in the presentinvention. Therefore the description of DOE herein should not be used tolimit the scope of the present invention.

In order to clearly describe the invention in detail, the following foursubsections are included: 1) description of the assembly processcategorization method; 2) description of the search pattern andparameter specification method; 3) description of the DOE-basedparameter optimization method and system; 4) description of the resultsatisfaction criteria and second round optimization strategy; along with5) a description of on-pendant parameter setup and optimization methodand graphical user interface.

1) Description of the Robotic Assembly Process Categorization

Robotic assembly processes are categorized based on their nature intocylindrical, radial and multi-stage insertion/assembly. As illustratedin FIGS. 2a to 2c , cylindrical insertion includes assembly applicationswith a cylinder and a smooth bore, such as transmission valve bodyassembly and piston stuffing. More specifically, FIG. 2a shows the startposition of the assembly where the edge of cylinder 202 to be firstinserted into smooth bore 204, hereinafter the leading edge, ispositioned just above the opening of the bore 204. The assemblyapplication proceeds to the engage position shown in FIG. 2b where theleading edge of cylinder 202 is inserted part way into the bore 204. Theassembly application is finished when as is shown in FIG. 2c the leadingedge of cylinder 202 is in the inserted position in smooth bore 204.

Radial insertion, which is not shown, includes assembly applicationswith a “toothed”, “splined” or “gear” mesh, such as a forward clutchassembly and spline gear assembly. Multi-stage insertion, which canrequire any combination of cylindrical and radial insertions, includesassembly applications such as the assembly of a torque converterassembly 300 illustrated in FIGS. 3a to 3c and half shaft assembly (notshown). The component 301 in FIG. 3 illustrates the assembly contactpart of the torque converter that is held by the robot. The component301 has gear teeth shown by the wavy lines which are to be mated to theteeth shown by the straight lines in the component 302 of thetransmission shaft that is not held by the robot. FIG. 3a shows therelation between the assembled parts at starting time; FIG. 3b showsthat the first gear set which is the largest in component 301 is engagedwith the teeth in component 302; and FIG. 3c shows that the second gearset is engaged with those teeth.

The cylindrical, radial and multi-stage insertion types can cover mostof the real-world assembly applications. Additional insertion types canbe defined if there is an assembly process that can not be described bythe above types. For each insertion/assembly type, a certain searchpattern is applied to perform the assembly process. There are robotforce control parameters that are related to each search pattern. Thenext section describes the search pattern and specification of therelated robot force control parameters.

2) Description of Search Pattern and Robot Parameter Specification

It has been found that the type of assembly defines the assembly searchpattern. In other words, different insertion types need different searchpatterns to accomplish the job. For example, in cylindrical insertion, aspiral searching pattern 400, one example of which is shown in FIG. 4,is identified as the major search pattern along with circular, rotationand Z-direction hopping patterns. The final force control search path isdetermined by the combination of all of the search patterns with theirparameters. In radial insertion, the Z-direction rotation is thedominant search pattern along with the circular and Z-direction hoppingpattern. In multi-stage insertion, search patterns used in radialinsertion are applied for each of the multi stages. Besides the searchpattern, the transition state between the search patterns and thecombination of the patterns are also defined. All of those functions arerepresented by robotic assembly parameters.

There are two sets of assembly parameters in the system of the presentinvention. One set, called Set Parameter, is related to the searchpattern transition, process action and insertion termination and isnormally setup at the beginning of the robotic assembly engineeringprocess. The Set Parameters are:

Insertion Distance—the distance between the insertion starting positionand the end position (see FIG. 2c )

Engage Distance—the distance between the insertion starting position tothe position where the parts are engaged (see FIG. 2b )

Time Limit—time limitation before a search process is terminated orre-performed

Max Num Try—maximum number of trials before an unsuccessful insertion isclaimed

Use Timeout Act—a flag that signals whether or not to use a customerspecified timeout handling method

TO Action Num—timeout handling routine number

Use Force Cond—a flag that signals whether or not to use force controlinsertion termination

Cond Force Value—condition force value used in force control insertiontermination

Use IO Action—a flag that signals whether or not IO action is used atend of the insertion

IO Action Num—the IO number used in the action above

Use Force Retreat—a flag that signals whether or not the force controlretreat is used

Retreat Force—the force value used in the force control retreat

Another set of the parameters, called Tune Parameters, are more relatedto the performance tuning of the robotic assembly process. Tuneparameters are insertion/assembly type related and the initial valuesare setup at the beginning of the robotic assembly process. For acylindrical insertion type, the parameters can be but are not limited tothe following:

Search force—Max value of the searching/insertion force used in the toolZ direction

Spring Const—spring constant for the spring force used in the insertion

Spiral Speed—spiral searching speed in the X-Y plane of the tool frame

Spiral Radius—the maximum radius in which the spiral search is performed

Spiral Turn—the turn number in which the spiral search motion reachesits maximum radius

Circular Speed—the circular searching speed in the X-Y plane of the toolframe

Circular radius—the circular radius used in the insertion correspondingto the circular speed above

Force Amp—the amplitude of the hopping force in the Z direction of thetool frame

Force Period—the period of the hopping (oscillation) force

For radial type insertion, the tune parameters are:

Search force—Max value of the searching/insertion force used in the toolZ direction

Rotation Speed—the rotation speed used in the insertion around the toolZ axis

Rotation Angle—the maximum angle value used in the above rotation motion

Circular Speed—the circular searching speed in the X-Y plane of the toolframe

Circular Radius—the circular radius used in the insertion correspondingto the circular speed above

Force Amp—the amplitude of the hopping force in the Z direction of toolframe

Force Period—the period of the hopping (oscillation) force

The combination of the above parameterized force reference and theassembly contact force define the actual assembly path. An example ofthe radial type insertion reference path, that is the ideal path forradial insertion, is shown in FIG. 5. Reference path 500 has both arotational component shown by the arrow 502 and an up and downcomponents shown by the arrow 504. The zigzag lines 506 in FIG. 5 showthe final path. Reference numeral 508 designates the center for thecoordinate system and numeral 507 designates the robot tool center pointthat moves.

3) Description of the DOE-Based Parameter Optimization Method

DOE (Design Of Experiments) is a method that is used to find a set ofoptimal parameters symmetrically. Design of Experiments for Engineersand Scientists, Jiju Antony, Elsevier Butterworth-Heinemann, 2003 andDesign and Analysis of Experiments, Sixth Edition, Douglas C.Montgomery, John Wiley & Son, Inc. 2005 introduces the basic DOE conceptand analysis method and describes the formulation and calculationdetails for the DOE method.

As illustrated in FIG. 6, for a particular process, which can be any oneof the processes described above, the controllable variables such asproduction batch are defined, the uncontrollable variables are ignored,the inputs are varied in a designed manner, and the outputcharacteristics are measured. Normally, a fractional factorial test isused to find the most influential parameters first and then fullfactorial tests are used to result in a set of optimized parameters. Inthis robotic assembly parameter optimization, for example in the radialinsertion type, the input parameters are Search Force, Rotation Speed,Rotation Angle, Circular Speed, Circular Angle, Force Amp and ForcePeriod. The output variable is Assembly Time.

An experiment was performed using the method and system of the presentinvention. The data obtained from performing the experiment was analyzedusing the DOE Analysis of Variance (ANOVA) method. A short descriptionof the ANOVA method is given below along with the various screen shots,also described below, of a graphic user interface used in theexperimental setup. The PB screening method, also described below, isfirst employed to identify the most influential of the input parameters.

A Pareto plot named after the Italian scientist Vilfredo Pareto, shownin FIG. 13, is used to show visually the influence of the input TuneParameters. The Pareto plot is a way to present statistical data. Theplot allows the detection of the factor and interaction effects that aremost important to the process or design optimization study.

In the plot, A is the Search Force, B is the Rotation Speed, G is theForce Period, D is the Circular Speed, E is the Circular Radius, C isthe Rotation Angle and F is the Force Amplitude. As is shown in FIG. 13,the three most influential input parameters are the A, B and Gparameters. A full factorial test is used for optimizing the three mostinfluential parameters. The main effect plot of FIG. 15 shows the trendof each of the three parameters A, B and G. Finally, experiments are runfor the optimized parameter set repeatedly to find the statisticalproperty of the assembly performance. If multiple trials are run tominimize the variation that is introduced by the manufacturing processin parameter screening, mean value of the assembly time is used in theanalysis; and in parameter optimization, both mean value and mean valueplus 3-sigma is used in the analysis to consider the effect of thevariation. In addition, the success rate is calculated for each of allsets of the parameters. In the verification analysis, the assembly cycletime distribution is calculated and plotted as is shown in FIG. 17.

Analysis of Variance (ANOVA)

FIG. 7 shows a DOE analysis of variance diagram. The total variance(MST) is the combination of the variances, MSX1 to MSXn respectively, ofthe factors X1 to Xn shown in the dotted line in FIG. 7 and the randomvariance (MSE).

Suppose there are α levels of a single factor that are to be compared.The observed response from each of the α levels is a random variable.The data would be y_(ij), representing the jth observation taken underfactor level i. There will be, in general, n observations. Then:

$\begin{matrix}{y_{ij} = {\mu + \tau_{i} + {ɛ_{ij}\left\{ \begin{matrix}{{i = 1},2,\ldots,\alpha} \\{{j = 1},2,,\ldots,n}\end{matrix} \right.}}} & \left( {3\text{-}1} \right)\end{matrix}$

In this equation 3-1, μ is a parameter common to all levels called theoverall mean, and τ_(i) is a parameter unique to the ith level calledith level effect. ε_(ij) is a random error component that incorporatesall other source of variability in the experiment.

Equation 3-1 is called the single-factor analysis of variance (ANOVA).If the design of the experiment is a completely randomized design, theobjectives are to test appreciate hypotheses about the treatment meansand to estimate them. For hypotheses testing, the model errors areassumed to be normally and independently distributed random variableswith mean and zero and variance σ². The variance σ² is assumed to beconstant for all levels of the factor. This implies the observationsy _(ij) ˜N(μ+τ_(i),σ²)  (3-2)and that the observations are mutually independent.

PB Screening Designs and Pareto Plot

P-B designs are named after their inventors R. L. Packett and J. P.Burman. These designs are based on Hadamard matrices in which the numberof experiment runs is a multiple of four (4) i.e. N=4, 8, 12, 16 . . .and so on, where N is the number of runs. The purpose of screeningdesigns is to identify and separate out those factors that demandfurther investigation. The Pareto plot, one example of which is shown inthe previously described FIG. 13, is as is shown in that figure a way topresent statistical data.

Normal Distribution, Mean and Variance

Normal distribution is the most important sampling distribution. Ifvariable y is a normal random variable, its probability distributionwill be a normal distribution.

$\begin{matrix}{{f(y)} = {\frac{1}{\sigma\sqrt{2\pi}}{\mathbb{e}}^{- {{({1\text{/}2})}{\lbrack{{({y - \mu})}\text{/}\sigma}\rbrack}}^{2}}}} & \left( {3\text{-}3} \right)\end{matrix}$where −∞<μ<+∞ is the mean of the distribution and σ²>0 is the variance.4) Description of the Result Satisfaction Criteria and OptimizationStrategy

In the most influential parameter screening test, all parameters (forexample the seven shown in FIG. 11 for radial type insertion) arescreened. As is described above, the results of the screening are shownin the Pareto plot of FIG. 13. If multiple trials are conducted for thesame test, the mean value is used in the screening analysis.Interactions among the parameters are not considered. Real-worldapplications showed that this assumption is valid for most of theassembly applications. In parameter optimization, the one (1) to three(3) most influential parameters and two (2) to five (5) levels, that is,amplitudes for the parameters were used. FIG. 14 shows one example ofthe parameter optimization in which the three most influential inputparameters A, B and G identified in the Pareto plot of FIG. 13 are usedwith three levels. The main effect plot of FIG. 15 shows the trend ofeach of the three parameters. FIG. 16 shows the results of theverification and FIG. 17 shows for the verification results a plot ofthe assembly time distribution. If the overall result is not satisfied,then as is shown in the flowchart 100 of FIG. 1 other parameters can beused for the next round of optimization until the assembly cycle timegoal is reached. If multiple trials are conducted for the same parameterset, both mean value and mean plus 3-Sigma as well as the success rateare considered in the optimal parameter set searching.

5) Description of the On-Pendant Parameter Setup and Optimization andGraphical User Interface.

FIG. 18 shows a system that can be used to implement the presentinvention. While all robotic assembly parameters including resultingassembly cycle time are stored in and testing runs are performed by arobot controller such as controller 1804 of FIG. 18, the parameteroptimization process is performed on the teach pendant 1810 of FIG. 18in order to improve the time performance. A software routine is writtento be executed in a micro processor in the robot teach pendant 1810.

In addition, a graphic user interface (GUI) is developed to handle thetesting variable setup, program execution, data analysis and resultdisplay. FIG. 8 shows a main page of the user interface; FIG. 9 showsthe robot position setup page; FIGS. 10 and 11 illustrate the set andtune parameter setup pages. FIGS. 12 and 13 display the screening setupand result. FIGS. 14 and 15 display the optimization setup and result,and FIGS. 16 and 17 describe the parameter verification setup andresult.

As will be appreciated by one of skill in the art, the present inventionmay be embodied as a method, system, or computer program product.Accordingly, the present invention may take the form of an entirelyhardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module” or “system.”

Furthermore, the present invention may take the form of a computerprogram product on a computer-usable or computer-readable medium havingcomputer-usable program code embodied in the medium. The computer-usableor computer-readable medium may be any medium that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, or deviceand may by way of example but without limitation, be an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, device, or propagation medium or even be paper or othersuitable medium upon which the program is printed. More specificexamples (a non-exhaustive list) of the computer-readable medium wouldinclude: an electrical connection having one or more wires, a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), an optical fiber, a portable compact disc read-onlymemory (CD-ROM), an optical storage device, a transmission media such asthose supporting the Internet or an intranet, or a magnetic storagedevice.

Computer program code for carrying out operations of the presentinvention may be written in an object oriented programming language suchas Java, Smalltalk, C++, C# or the like, or may also be written inconventional procedural programming languages, such as the “C”programming language. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough a local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider).

Referring now to FIG. 18, there is shown a system 1800 which may be usedto implement the present invention. The system 1800 includes the method1802 of FIG. 18 and 100 of FIG. 1 which may be resident, as describedabove, on a suitable media in a form that can be loaded into the robotcontroller 1804 for execution. Alternatively, the method can be loadedinto the controller 1804 or may be downloaded into the controller 1804,as described above, by well known means from the same site wherecontroller 1804 is located or at another site that is remote from thesite where controller 1804 is located. As another alternative, themethod 1802 may be resident in controller 1804 or the method 1802 mayinstalled or loaded into a computing device, such as the teach pendant1810 that as described above has a microprocessor or a PC, which isconnected to controller 1804 to send commands to the controller 1804.The GUI described above would then be resident on the teach pendant 1810or on the PC.

As can be appreciated by those of ordinary skill in the art, when themethod is in controller 1804, the controller functions as a computingdevice to execute the method 1802. The controller 1804 is connected torobot 1806 which in turn is used to perform the assembly process 1808.Thus if the method 1802 is executed by controller 1804 or if thecontroller 1804 receives commands from a computing device such as teachpendant 1810 that executes the method 1802, the robot 1806 is controlledto perform the assembly process 1808 in accordance with the presentinvention. It should be appreciated that the method 1802 can beimplemented on the robot controller 1804 as a software product, orimplemented partly or entirely on a remote computer, which communicateswith the robot controller 1804 via a communication network, such as, butnot limited to, the Internet.

It is to be understood that the description of the foregoing exemplaryembodiment(s) is (are) intended to be only illustrative, rather thanexhaustive, of the present invention. Those of ordinary skill will beable to make certain additions, deletions, and/or modifications to theembodiment(s) of the disclosed subject matter without departing from thespirit of the invention or its scope, as defined by the appended claims.

What is claimed is:
 1. A method of optimizing the parameters ofcontrolling a robot to fit a fitting part into a receiving part, saidmethod comprising: categorizing independent of a size of said fittingpart and a size of said receiving part an assembly process for saidrobot to fit said fitting part into said receiving part as one of aplurality of predetermined types of said assembly processes; specifyingfrom said categorized assembly process a search pattern for fitting saidfitting part into said receiving part and parameters for saidcategorized assembly process; obtaining by using a predeterminedtechnique a set of optimal parameters for said categorized assemblyprocess; verifying that said set of optimal parameters meets apredetermined criteria to become the optimal parameters for controllingsaid robot to perform said search pattern specified from saidcategorized assembly process; and controlling said robot to perform saidspecified search pattern using said verified optimal parameters for saidcategorized assembly process to create an assembly that is a combinationof said fitting and receiving parts.
 2. The method of claim 1 furthercomprising selecting said categorized assembly process from saidpredetermined types of assembly processes comprising cylindrical, radialand multi-stage insertion assembly processes.
 3. The method of claim 1further comprising assembling said specified search pattern from aplurality of search patterns with a transition state between each ofsaid plurality of patterns and said parameters specified for saidcategorized assembly process comprises a set of parameters related tosaid search pattern transitions and another set of parameters related tothe performance of said categorized assembly process.
 4. The method ofclaim 2 wherein said multi-stage insertion assembly process is acombination of one or more cylindrical assembly processes and one ormore radial assembly processes.
 5. The method of claim 2 wherein in saidcylindrical assembly process said fitting part is cylindrical and saidreceiving part is a smooth bore.
 6. The method of claim 2 wherein saidradial assembly process includes assembly applications with a toothed,splined or gear mesh.
 7. The method of claim 1 wherein said fitting partand said receiving part each have a predetermined structure and saidmethod further comprises categorizing said assembly process based onsaid fitting part predetermined structure and said receiving partpredetermined structure.
 8. The method of claim 1 wherein said assemblyprocess for said robot to fit said fitting part into said receiving partis one or more of said plurality of predetermined types of said assemblyprocesses and said method further comprises categorizing based on saidfitting part predetermined structure and said receiving partpredetermined structure which of said one or more of said plurality ofassembly processes are to be used by said robot to fit said fitting partinto said receiving part.
 9. A method of optimizing the parameters ofcontrolling a robot to fit a fitting part into a receiving part, saidmethod comprising: categorizing from a plurality of predetermined typesof assembly processes independent of a size of said fitting part and asize of a receiving part an assembly process for said robot to fit saidfitting part into said receiving part to create an assembly that is acombination of said fitting and receiving parts; specifying from saidcategorized assembly process a search pattern for said categorizedassembly process and parameters for said categorized assembly processfor fitting said fitting part into said receiving part to create saidassembly that is said combination of said fitting and receiving parts;obtaining by using a predetermined technique a set of optimal parametersfor said categorized assembly process; verifying that said set ofoptimal parameters meets a predetermined criteria to become the optimalparameters for controlling said robot to perform said search patternspecified from said categorized assembly process; and controlling saidrobot to perform said specified search pattern for fitting said fittingpart into said receiving part to create said assembly that is saidcombination of said fitting and receiving parts using said verifiedoptimal parameters for said categorized assembly process.
 10. The methodof claim 9 further comprising selecting said categorized assemblyprocess from said predetermined types of assembly processes comprisingcylindrical, radial and multi-stage insertion assembly processes. 11.The method of claim 9 further comprising assembling said specifiedsearch pattern from a plurality of search patterns with a transitionstate between each of said plurality of patterns and said parametersspecified for said categorized assembly process comprises a set ofparameters related to said search pattern transitions and another set ofparameters related to the performance of said categorized assemblyprocess.
 12. The method of claim 10 wherein said multi-stage insertionassembly process is a combination of one or more cylindrical assemblyprocesses and one or more radial assembly processes.
 13. The method ofclaim 10 wherein in said cylindrical assembly process said fitting partis cylindrical and said receiving part is a smooth bore.
 14. The methodof claim 10 wherein said radial assembly process includes assemblyapplications with a toothed, splined or gear mesh.
 15. The method ofclaim 9 wherein said fitting part and said receiving part each have apredetermined structure and said method further comprises categorizingsaid assembly process based on said fitting part predetermined structureand said receiving part predetermined structure.
 16. The method of claim9 wherein said assembly process for said robot to fit said fitting partinto said receiving part is one or more of said plurality ofpredetermined types of said assembly processes and said method furthercomprises categorizing based on said fitting part predeterminedstructure and said receiving part predetermined structure which of saidone or more of said plurality of assembly processes are to be used bysaid robot to fit said fitting part into said receiving part.
 17. Amethod of optimizing the parameters of controlling a robot to fit afitting part into a receiving part, said method comprising: categorizingbased on a characteristic of said fitting part and said receiving partother than a size of said fitting part and a size of said receiving partan assembly process for said robot to fit said fitting part into saidreceiving part as one of a plurality of predetermined types of saidassembly processes; specifying from said categorized assembly process asearch pattern for fitting said fitting part into said receiving partand parameters for said categorized assembly process; obtaining by usinga predetermined technique a set of optimal parameters for saidcategorized assembly process; verifying that said set of optimalparameters meets a predetermined criteria to become the optimalparameters for controlling said robot to perform said search patternspecified from said categorized assembly process; and controlling saidrobot to perform said specified search pattern using said verifiedoptimal parameters for said categorized assembly process to create anassembly that is a combination of said fitting and receiving parts. 18.The method of claim 17 wherein said fitting part and said receiving parteach have a predetermined structure and said method further comprisescategorizing said assembly process based on said fitting partpredetermined structure and said receiving part predetermined structure.19. The method of claim 17 wherein said assembly process for said robotto fit said fitting part into said receiving part is one or more of saidplurality of predetermined types of said assembly processes and saidmethod further comprises categorizing based on said fitting partpredetermined structure and said receiving part predetermined structurewhich of said one or more of said plurality of assembly processes are tobe used by said robot to fit said fitting part into said receiving part.